Abstract
The gearboxes are among the most important elements of rotating machines and consequently they require an effective condition monitoring strategy. However, many machines operate over a wide range of the rotational speed and most analysis of rotating machines are based on investigating the vibrations with a constant speed. Therefore, techniques developed for constant conditions cannot be applied directly.
The angularly sampled Instantaneous Angular Speed (IAS) carry a considerable amount of information on the health and usage status of rotating machinery. Thus, it represents a potential source of relevant information in intelligent fault detection and diagnosis systems, but also to construct Feature Vector (FV) to further get robust and effective classification methods for different running speed or load conditions.
This paper presents an intelligent gear fault diagnosis based on Instantaneous Angular Speed (IAS), Differential Evolution (DE) and multi-class Support Vector Machine (SVM) in normal and non-stationary conditions. For this purpose, features are extracted from IAS. Then, the DE selection algorithm is applied in order to select the most relevant features. The classification is performed by SVM in order to improve the detection and identification of gear defects. The methodology is applied in normal and non-stationary conditions, with six pinion fault conditions. The experimental results prove that the proposed method is able to detect the fault conditions of the gearbox effectively.
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Acknowledgments
This work was achieved at the laboratories LaMCoS (INSA - Lyon, France) and LMPA (IOMP, Sétif -1- University, Algeria). The authors would like to thank the Algerian and French Ministries of Higher Education and Scientific Research for their financial and technical support in the framework of program PROFAS 2012.
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Fedala, S., Rémond, D., Felkaoui, A., Selmani, H. (2019). Intelligent Gear Fault Diagnosis in Normal and Non-stationary Conditions Based on Instantaneous Angular Speed, Differential Evolution and Multi-class Support Vector Machine. In: Felkaoui, A., Chaari, F., Haddar, M. (eds) Rotating Machinery and Signal Processing. SIGPROMD’2017 2017. Applied Condition Monitoring, vol 12. Springer, Cham. https://doi.org/10.1007/978-3-319-96181-1_2
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